Automatic and quantitative assessment of regional muscle volume by multi-atlas segmentation using whole-body water–fat MRI
2015 (English)In: Journal of Magnetic Resonance Imaging, ISSN 1053-1807, E-ISSN 1522-2586, Vol. 41, no 6, 1558-1569 p.Article in journal (Refereed) Published
To develop and demonstrate a rapid whole-body magnetic resonance imaging (MRI) method for automatic quantification of total and regional skeletal muscle volume.
Materials and Methods
The method was based on a multi-atlas segmentation of intensity corrected water–fat separated image volumes. Automatic lean muscle tissue segmentations were achieved by nonrigid registration of atlas datasets with 10 different manually segmented muscle groups. Ten subjects scanned at 1.5 T and 3.0 T were used as atlases, initial validation and optimization. Further validation used 11 subjects scanned at 3.0 T. The automated and manual segmentations were compared using intraclass correlation, true positive volume fractions, and delta volumes.
For the 1.5 T datasets, the intraclass correlation, true positive volume fractions (mean ± standard deviation, SD), and delta volumes (mean ± SD) were 0.99, 0.91 ± 0.02, −0.10 ± 0.70L (whole body), 0.99, 0.93 ± 0.02, 0.01 ± 0.07L (left anterior thigh), and 0.98, 0.80 ± 0.07, −0.08 ± 0.15L (left abdomen). The corresponding values at 3.0 T were 0.97, 0.92 ± 0.03, −0.17 ± 1.37L (whole body), 0.99, 0.93 ± 0.03, 0.03 ± 0.08L (left anterior thigh), and 0.89, 0.90 ± 0.04, −0.03 ± 0.42L (left abdomen). The validation datasets showed similar results.
The method accurately quantified the whole-body skeletal muscle volume and the volume of separate muscle groups independent of field strength and image resolution.
Place, publisher, year, edition, pages
John Wiley & Sons, 2015. Vol. 41, no 6, 1558-1569 p.
multi-atlas segmentation; muscles; registra- tion; muscle volume; classification; MRI
Radiology, Nuclear Medicine and Medical Imaging Medical Image Processing
IdentifiersURN: urn:nbn:se:liu:diva-109319DOI: 10.1002/jmri.24726ISI: 000354738100008PubMedID: 25111561OAI: oai:DiVA.org:liu-109319DiVA: diva2:737447